Open-source alternatives to UETorch

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

This project is an AI research implementation library and machine learning research repository. It provides a collection of reference code, illustrative implementations, and open-source research datasets used to verify hypotheses and build upon existing models in artificial intelligence. The repository focuses on scientific research reproduction by translating theoretical findings from published papers into executable code. It includes specialized scientific simulation environments designed to test the behavior of autonomous agents and models within controlled settings. The project covers AI

CARLA is an autonomous driving simulator and research environment designed for developing and validating self-driving software. It functions as an urban traffic simulator that generates realistic vehicle and pedestrian behavior and as a synthetic sensor data generator producing LiDAR, Radar, and camera data. The platform distinguishes itself through its deep integration with robotics frameworks, specifically providing native connectivity to ROS2 nodes for robotic control and data processing. It supports the training of driving models via imitation and reinforcement learning within a controlle

Generate random crop fields for Ignition Gazebo

Unreal Engine Plugin to enable ROS Support

Support for ROS-enabled RGBD data acquisition in Unreal Engine Projects

Webots is a physics-based robot simulator and development environment used for modeling, programming, and testing the behavior of robots in a simulated 3D physical world. It serves as a virtual prototyping tool to verify mechanical and electronic systems through the creation of virtual robot models and control logic. The platform enables a full robotics simulation workflow, including the development of robot controllers and the programming of autonomous agent behaviors. It focuses on physical system modeling to represent the mechanical properties of hardware and simulate real-world interactio

Deepdrive is a simulator that allows anyone with a PC to push the state-of-the-art in self-driving

A Platform for Many-Agent Reinforcement Learning

An OpenAI gym wrapper for CARLA simulator

👉 CARLA resources such as tutorial, blog, code and etc https://github.com/carla-simulator/carla

Cloe

MIT Cheetah I Implementation

SUMO is a microscopic traffic simulation suite designed to model the movement of individual vehicles and pedestrians across large-scale urban road networks. It functions as a multimodal transport simulator that integrates cars, pedestrians, bicycles, railways, and waterways within a single environment, supported by tools for road network generation and traffic demand modeling. The project distinguishes itself through specialized toolsets for environmental impact analysis, which calculate vehicle emissions and energy consumption for electric and hybrid fleets. It provides comprehensive capabil

A minimalist environment for decision-making in autonomous driving

Modular autonomous driving platform running on the CARLA simulator and real-world vehicles.

Simulation for spacecraft attitude control system analysis and design

Refer to https://github.com/AcutronicRobotics/gym-gazebo2 for the new version

a basic OpenSCENARIO player

Habitat-Lab

TensorFlow Reinforcement Learning

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Computational framework for reinforcement learning in traffic control

Lab is a customizable 3D platform and research testbed designed for training and testing autonomous agents using reinforcement learning. It serves as a spatial AI training simulator where agents can be evaluated through navigation and puzzle-solving tasks. The environment allows for the definition of complex layouts and task behaviors through external scripting, enabling the generation of specific challenges for AI research. It supports both automated training via standard API bindings and manual agent control to validate simulation dynamics. The system utilizes a grid-based spatial represen

ROS bridge for CARLA Simulator